Zobrazeno 1 - 10
of 219 067
pro vyhledávání: '"A. A. Curtis"'
Autor:
Pyo, Andrew G. T., Nagano, Yuta, Milighetti, Martina, Henderson, James, Callan Jr., Curtis G., Chain, Benny, Wingreen, Ned S., Tiffeau-Mayer, Andreas
The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules
Externí odkaz:
http://arxiv.org/abs/2412.13722
The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to critical failures. Group Test
Externí odkaz:
http://arxiv.org/abs/2412.11073
Autor:
Song, Zhigang, Zhang, Xiuying, Klein, Julian, Curtis, Jonathan, Ross, Frances M., Narang, Prineha
Momentum-resolved spin-polarized bands are a key ingredient in many proposed spintronic devices, but their existence often relies on lattice commensurability or strong spin-orbit coupling. By a large-scale DFT calculation (up to 4212 atoms), we propo
Externí odkaz:
http://arxiv.org/abs/2412.09192
Autor:
Baer-Way, Raphael, Chandra, Poonam, Modjaz, Maryam, Kumar, Sahana, Pellegrino, Craig, Chevalier, Roger, Crawford, Adrian, Sarangi, Arkaprabha, Smith, Nathan, Maeda, Keiichi, Nayana, A. J., Filippenko, Alexei V., Andrews, Jennifer E., Arcavi, Iair, Bostroem, K. Azalee, Brink, Thomas G., Dong, Yize, Dwarkadas, Vikram, Farah, Joseph R., Howell, D. Andrew, Hiramatsu, Daichi, Hosseinzadeh, Griffin, McCully, Curtis, Meza, Nicolas, Newsome, Megan, Gonzalez, Estefania Padilla, Pearson, Jeniveve, Sand, David J., Shrestha, Manisha, Terreran, Giacomo, Valenti, Stefano, Wyatt, Samuel, Yang, Yi, Zheng, WeiKang
While the subclass of interacting supernovae with narrow hydrogen emission lines (SNe IIn) consists of some of the longest-lasting and brightest SNe ever discovered, their progenitors are still not well understood. Investigating SNe IIn as they emit
Externí odkaz:
http://arxiv.org/abs/2412.06914
Autor:
Bluethgen, Christian, Van Veen, Dave, Zakka, Cyril, Link, Katherine, Fanous, Aaron, Daneshjou, Roxana, Frauenfelder, Thomas, Langlotz, Curtis, Gatidis, Sergios, Chaudhari, Akshay
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describin
Externí odkaz:
http://arxiv.org/abs/2412.01233
Rigid multi-link robotic arms face a tradeoff between their overall reach distance (the workspace), and how compactly they can be collapsed (the storage volume). Increasing the workspace of a robot arm requires longer links, which adds weight to the
Externí odkaz:
http://arxiv.org/abs/2412.00268
Autor:
Sengupta, Saptarshi, Curtis, Kristal, Mallipeddi, Akshay, Mathur, Abhinav, Ross, Joseph, Gou, Liang
Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training an LLM is not always feasible because of the scarcity of domain data,
Externí odkaz:
http://arxiv.org/abs/2412.04494
Autor:
Paschali, Magdalini, Chen, Zhihong, Blankemeier, Louis, Varma, Maya, Youssef, Alaa, Bluethgen, Christian, Langlotz, Curtis, Gatidis, Sergios, Chaudhari, Akshay
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on
Externí odkaz:
http://arxiv.org/abs/2411.18730
Autor:
Prakash, Eva, Valanarasu, Jeya Maria Jose, Chen, Zhihong, Reis, Eduardo Pontes, Johnston, Andrew, Pareek, Anuj, Bluethgen, Christian, Gatidis, Sergios, Olsen, Cameron, Chaudhari, Akshay, Ng, Andrew, Langlotz, Curtis
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials a
Externí odkaz:
http://arxiv.org/abs/2411.18602
Autor:
McDonald, Curtis, Barron, Andrew R.
This paper presents a Bayesian estimation procedure for single hidden-layer neural networks using $\ell_{1}$ controlled neuron weight vectors. We study the structure of the posterior density that makes it amenable to rapid sampling via Markov Chain M
Externí odkaz:
http://arxiv.org/abs/2411.17667